MANAGING POPULATIONS WITH AN

AUTOMATED CLINICAL RISK SYSTEM

Session #304, February 15, 2019

John Dunlap, DO, CPHQ, CCHP, Deputy Medical Executive, California Correctional Health Care Services

Michael Selby, MBA, Health Program Manager III, California Correctional Health Care Services

John Dunlap, DO, CPHQ, CCHP

I have no conflicts of interest to report. I have no financial

interest in any device, software, corporation, or research firm.

Michael Selby, MBA

I have no conflicts of interest to report. I have no financial

interest in any device, software, corporation, or research firm.

CONFLICT OF INTEREST

• Identify the healthcare factors utilized by CCHCS to stratify clinical

risk

• Translate the change management strategy used by CCHCS to

implement the Automated Clinical Risk Classification System for

use in your organization’s informatics projects

• Recognize the benefits of co-locating clinical subject matter

experts with programmers

LEARNING OBJECTIVES

A G E N D A

• LOCAL PROBLEM

• DESIGN & IMPLEMENTATION

• HOW IT SOLUTION WAS UTILIZED

• VALUE DERIVED

L O C A L P R O B L E M

P R O B L E M S T A T E M E N T

High risk population are not consistently and accurately identified and placed throughout

their incarceration to receive the right level of resources.

B B

E V O L U T I O N O F M E D I C A L C L A S S I F I C A T I O N

2006

Receivership

Began

2009

Medical

Classification

System Introduced

2012

Medical

Classification

e-Form Introduced

2013

Automated Clinical

Risk Classification

Implemented

Healthcare Factors Introduced

• Rules Broadly Defined Risk & Open To

Interpretation

• Often Inaccurate

• Often Not Up To Date

• Person Dependent Paper Process in

Communicating to Custody

Little to no

consideration for

Healthcare Factors

Communication to Custody Improved

• Paper Process Eliminated, But Still

Person Dependent

• Rules Still Not Standardly Applied

• Inaccuracies Persisted

• Still Not Updated Timely

Provider conducts

chart review to

determine clinical risk.

Provider manually enters

clinical risk into Medical

Classification e-Form.

e-Form transmits information

to SOMS; custody uses

healthcare factors to

appropriately house inmate.

When clinical status

changed: Provider

manually updates e-Form.

P R I O R T O N E W S O L U T I O N . . .

Repeated audits found discrepancies between reported risk and actual risk.

• Only 62% of High Risk Patients were Properly Placed

• 6/2013 Hospital Readmission Rate

20% High Risk vs. 14% Total Population

• 6/2015 Potentially Avoidable Hospitalizations (per 1,000)

71 High Risk vs. 11 Total Population

B A S E L I N E D A T A

IMPROVE CARE & REDUCE COSTS

CLINICAL

• Increase Appropriate Placement of High

Risk Inmates

• Reduce Preventable Morbidity & Mortality

LEVERAGE IT & DATA

ADMINISTRATIVE

• Automate Complicated Rules for Clinical

Risk

• Provide Timely, Actionable Information

• Provide Near Real-Time Performance

Reports

E X P E C T E D P R O J E C T O U T C O M E S

D E S I G N &

I M P L E M E N T A T I O N

E V A L U A T I N G I T S O L U T I O N A L T E R N A T I V E S S

COTS SOLUTIONS WERE NOT VIABLE

• Risk stratification applications based on Claims Data from PCPs

– CCHCS PCPs Don’t Bill for Services

• Unique requirements for correctional setting

EXISTING RESOURCES

• E-Form Application

• Healthcare Data Warehouse – SQL

• In-House Talent (Clinical and Technical)

D E V E L O P I N G T H E I T W O R K F L O W

Patient

Demographics

Q 12 HOURS

HEALTHCARE DATAWAREHOUSE

Automated job runs to refresh data tables

CUSTODY

Notified of Clinical Risk

changes automatically

SOMS

eFORM

APPLICATION

Health Records

Claims

Diagnostics

DEFINE RULES FOR EACH FACTOR

DEFINE HIGH RISK FACTORS

DEFINE RISK LEVELS & AVAILABLE SOURCES

DEFINE DATA ELEMENTS & SOURCES FOR FACTORS

• Medium Risk Outpatient

• Low Risk Outpatient

• High Risk + Inpatient/Specialized Care Needs

• High Risk Outpatient

• Chronic Medical Conditions & Degree of Control

• Hospitalizations/Emergency Dept. Encounters

• 4 or more conditions; >1 problem prone condition

• Advanced Age- >65

• Problem List Entries

• Custody Demographic Information

• Age

• Medications

• Polypharmacy (12+ Medications)

• 2+ Hospital/3+ ED Encounters

• Pharmacy and Diagnostic Information Systems

• Claims Data

H O W U S E R S S U P P O R T E D S O L U T I O N

& W O R K F L O W D E V E L O P M E N T

H O W U S E R S S U P P O R T E D S O L U T I O N

& W O R K F L O W D E V E L O P M E N T

H O W U S E R S S U P P O R T E D S O L U T I O N

& W O R K F L O W D E V E L O P M E N T

PARTNERING BUSINESS OWNER WITH IT DEVELOPERS

PROGRAMMERS

CLINICAL SMEs

H O W I T S O L U T I O N

W A S U T I L I Z E D

I T S O L U T I O N F U N C T I O N A L I T Y

I T S O L U T I O N F U N C T I O N A L I T Y C O N T .

C L I N I C A L W O R K F L O W I M P A C T E D

Provider conducts

chart review to

determine clinical

risk.

Provider manually enters

clinical risk into Medical

Classification e-Form.

e-Form transmits

information to SOMS;

custody uses healthcare

factors to appropriately

house inmate.

When clinical status changed:

Provider manually updates e-

Form.

I T S O L U T I O N W I T H I N T H E W O R K F L O W

Custody Staff

reviews Custody

Registry

Correctional

Counselor reviews

patient’s record

Correctional Counselor

determines appropriate

housing

Custody

moves patient

Risk Level

Automatically

Updated

I T S O L U T I O N B L U E P R I N T

DIAGNOSES

LAB DATA

PHARMACY

DATA

PATIENT

MEDICAL

CONDITIONS

PATIENT

OVERALL

RISK LEVEL

LOW

HIGH 2

MEDIUM

HIGH 1

HEALTHCARE

TOOLS

DASHBOARD

MEASURE

CUSTODY

REGISTRY

CREATE INFRASTRUCTURE

DESIGN OUTPUT

DEVELOP TOOLS

PATIENT

DEMOGRAPHICS

C C H C S H E A L T H C A R E D A T A W A R E H O U S E

DATA

WAREHOUSE

• Metadata

• Raw Data

• Summary Data

AD HOC ANALYSIS

REPORTING

DATA MINING

EHRS

SOMS

LEGACY

SYSTEMS

FLAT FILES

ETL

Extract

Transform

Load

C H A N G E M A N A G E M E N T S T R A T E G Y

• Organizational memorandum from leadership

• Executive forums at all levels of the organization to communicate this change

• Refine existing policy and procedures

• Define process workflows

• Override process

• Decision support

• Training materials

• Status reports post implementation to see progress upon initial implementation

• Statewide high risk performance report

C H A N G E M A N A G E M E N T S T R A T E G Y

PROVIDING ACCESS TO END-USERS – LINKS FROM CDCR INTRANET TO QM TOOLS

V A L U E D E R I V E D

P O S T I M P L E M E N T A T I O N P R O C E S S

10%

20%

30%

40%

50%

60%

70%

80%

90%

6/1/13

9/1/13

12/1/13

3/1/14

6/1/14

9/1/14

12/1/14

3/1/15

6/1/15

9/1/15

12/1/15

3/1/16

6/1/16

9/1/16

12/1/16

3/1/17

6/1/17

9/1/17

12/1/17

3/1/18

6/1/18

18%

High Risk

Placement

I M P R O V E M E N T S A C H I E V E D

10%

20%

30%

40%

50%

60%

70%

80%

90%

6/1/13

9/1/13

12/1/13

3/1/14

6/1/14

9/1/14

12/1/14

3/1/15

6/1/15

9/1/15

12/1/15

3/1/16

6/1/16

9/1/16

12/1/16

3/1/17

6/1/17

9/1/17

12/1/17

3/1/18

6/1/18

High Risk Rules

Modified and

Conversion to ICD-10

P O S T I M P L E M E N T A T I O N O U T C O M E

10%

15%

20%

25%

Statewide Hospital Readmissions

June 2013 – December 2017

All Hospital Readmissions High Risk Hospital Readmissions

June 2013 14% 20%

December 2017

12% 16%

P O S T I M P L E M E N T A T I O N O U T C O M E

Statewide Potentially Avoidable Hospitalizations (per 1,000)

June 2015 – December 2017

All PAH High Risk PAH

All Potentially Avoidable

Hospitalizations

High Risk Potentially Avoidable

Hospitalizations

June 2015 10.8 70.9

December 2017

8.9 43.5

E S T I M A T E D C O S T

FIXED COSTS

Costs

(

Storage, Software)

$525,000/year

DBA/DBD Contract

$125,000/year

Total

$650,000/year

DEVELOPMENT LABOR COSTS

QM Programmer

$90,000

Clinical SME

$62,500

Total

$152,500

Note: Fixed costs are associated to HDW & all applications; not specific to this solution

M&O LABOR COSTS

Programmer/year

$10,000

Clinical SME/year

$20,000

Total

$30,000

C O S T S A V I N G S

ESTIMATED COSTS SAVED BY AUTOMATING RISK LEVEL

– 230 Clinical Risk Changes per day x 5 minutes

clinician would spend reviewing patient’s chart

– 12 e-Forms per hour x $100 per hour (for P&S

classification)

– $1900 in saved time per day

– $2.1 million saved over 3 years, and growing

E X T E R N A L S T U D I E S C O M P A R I S O N S

Many models use the following variables:

 Age

 Gender

 Drugs

 Diagnoses

Some also use:

 Disability/functional status

 Procedures

Insurance type

Region

Employment info

Cost/use data

Not many (or none) use the following that we include:

 Lab values

 Provider specialty

None use as many factors as we do (for better or worse)

U S E R F E E D B A C K

L E S S O N S L E A R N E D

• Data is an asset that should be managed and leveraged

• Risk Classification should not be static

o Need process for resolving disputes

o Rules should be reevaluated periodically

• With the right IT infrastructure a healthcare organization can start to do

large scale improvement efforts

• Timeframe for goal overly ambitious

o Competing placement factors

o Patient population & individual clinical risk change over time

• Change management strategy may need to be revisited

o Focus and engage with custody stakeholders more

Q U E S T I O N S ?